Free Download Coursera - High-Performance and Parallel Computing Specialization
Released 9/2025
By Thomas Hauser and Shelley Knuth - University of Colorado Boulder
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 73 Lessons ( 6h 32m ) | Size: 1 GB
High-Performance and Parallel Computing. Learn to write efficient, scalable programs and optimize performance for high-performance computing.


What you'll learn
Explain key concepts in high-performance and distributed computing, including system architecture and parallel programming models.
Analyze and evaluate program performance and scalability in HPC environments.
Apply optimization techniques such as compiler options, vectorization, and OpenMP to improve program efficiency.
Design and implement parallel algorithms with MPI to build scalable applications.
Skills you'll gain
Operating Systems
Communication Systems
Linux
Cloud Development
Scripting
Big Data
Programming Principles
Distributed Computing
Computer Architecture
Scalability
Performance Tuning
C and C++
Unlock the power of modern computing systems with this hands-on specialization designed for scientists, engineers, scholars, and technical professionals. Whether you're working with large datasets, building machine learning models, or running complex simulations, high-performance computing (HPC) skills can significantly accelerate your work.
Throughout the specialization, you'll build a strong foundation in parallel and distributed computing. You'll start by learning the basics of Linux environments, shell scripting, and strategies for writing optimized code. You'll explore how to identify and resolve performance bottlenecks using profiling tools and gain a high-level understanding of modern HPC and cloud architectures. Finally, you'll dive into parallel programming using the Message Passing Interface (MPI) for scalable code in distributed systems.
By the end of this specialization, you'll be equipped with practical tools and techniques to write efficient, scalable code for high-performance environments. Prior experience with C, C++, or Python is recommended.
Applied Learning Project
Throughout the specialization, you'll complete a series of practical programming assignments in C++. These projects are designed to reinforce core concepts in high-performance and parallel computing, including code optimization, profiling, and message passing. By applying what you learn in real-world coding scenarios, you'll gain confidence and experience writing efficient, scalable programs for HPC environments.
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